Perioperative ANesthesia & SURgical Assessment System
Generalized accurate prediction of postoperative outcomes is almost impossible with random controlled trials, because there are simply too many factors influencing each postoperative outcome. This was eloquently formulated in 1996 by Prof. Nick Black.
… “most interventions have many components. Consider a simple surgical operation: this entails preoperative tests, anesthesia, the surgical approach, wound management, post-operative nursing, and discharge practice. And these are just the principal components. It will only ever be practical to subject a limited number of items to experimental evaluation.”
Furthermore, these factors differ between different institutions within each region, as well as between countries. The only way to solve this problem is to analyze the outcomes per institution — to use observational science (Black 1996).
PANSURAS solves these problems with a unique standarized observational data collection system, together with a semi-automated machine-learning process for each each individual institution where it is used. This may seem a “black box” labelled “machine-learning” being applied to analyze data from each institution, but the reality is more prosaic. Machine-learning is no more than a collection of algorithms or computer programs to process data in a database according to rules set by the programmer(s). In the case of PANSURAS, these are transparent, readable, and readily understood code blocks for data extraction and multiple forms of statistical analysis.
PANSURAS is not a box of magical tricks. Initially it will not provide data relevant to the exact local situation when initially used. Instead, just as with all machine-learning it must first have sufficient data from the intitution where used before any risk surgical risk factors and coefficients for predictive equations can be derived. Therefore users in each institution are faced with an initial and subsequent phases.
The flow chart below shows how the user interface is an initial algorithmic decison support system based upon scoring systems and studies published in medical journals, while the output for individual patients is a combination of the latter with location-specific machine-learning.
After collection of sufficient data at the location where employed, and activation of the data extraction and statistical modules forming the machine-learning system:
As a result, after sufficient data is present in the database, PANSURAS generates individualized risk predictions SPECIFIC for that institution to users involved in patient assessment — something truly unique at this time, and making the analytic system of PANSURAS applicable worldwide.